The mission of the Stanford BSSR Pre-Doctoral Training Program at the Intersection of Data Sciences with Behavioral, Social, and Population Health Research is to develop a cross-campus collaborative training program to provide talented pre-doctoral students with advanced specialized training at the intersections of social and behavioral health science, social epidemiology, data science, and population health. We are building a new team that spans the Stanford campus including faculty from 6 schools and 11 departments. We will provide a transformative multi-disciplinary predoctoral training environment that draws mentors from diverse fields (health and social psychology, medical sociology, social epidemiology, communications, health economics, business, education, law) and quantitative disciplines (computer science, informatics, statistics). The graduates of our program will have rigorous training in their own scientific disciplines, combined with extensive expertise working on a broad range of innovative research projects that rely on data of primarily two types: (1) intensive or voluminous longitudinal data from mHealth, smartphone and sensor technologies or electronic health records, and/or (2) large and complex data from internet, commercial, health administrative records, large population databases, internet data and social media platforms, crowd sourcing, and citizen science data. Predoctoral trainees in their first or second year of graduate studies will be admitted from programs in health psychology, medical sociology, social/behavioral epidemiology, health economics or another social science discipline. We are requesting support for 5 pre-doctoral students per year whose training will last 2 or more years. They will emerge from the program with a thorough understanding of their own fundamental discipline combined with advanced expertise in cutting-edge statistical and computational methods for analyzing increasingly complex and multidimensional longitudinal sets. The training program components will include both department or discipline-specific training in addition to program-wide data science components including: (1) innovative curriculum, including specialized quantitative curriculum customized to the experience and background of each trainee; (2) a mentored research experience with a dual mentor model (one disciplinary mentor, one methodological mentor); (3) exposure to team science approaches to problem solving, including design thinking, cross-disciplinary collaborations, and team building; (4) experiential components, including availability of Stanford dry-lab rotations and short-term internships in Silicon Valley companies; (5) forums for intellectual exchanges; and (6) many opportunities to develop professional skills in grant writing and collaboration. BSSR graduates will have the capability to conduct cutting edge research on behavioral and social, and health issues, as well as prevention and treatment interventions that have the potential to reduce the risks of heart, lung, blood, and sleep (HLBS) disorders and improve outcomes among patients with these disorders.